Dataset Information

Diabetes Prevalence and Its Relationship With Education, Wealth, and BMI in 29 Low- and Middle-Income Countries.

ABSTRACT:

Objective

Diabetes is a rapidly growing health problem in low- and middle-income countries (LMICs), but empirical data on its prevalence and relationship to socioeconomic status are scarce. We estimated diabetes prevalence and the subset with undiagnosed diabetes in 29 LMICs and evaluated the relationship of education, household wealth, and BMI with diabetes risk.

Research design and methods

We pooled individual-level data from 29 nationally representative surveys conducted between 2008 and 2016, totaling 588,574 participants aged ≥25 years. Diabetes prevalence and the subset with undiagnosed diabetes was calculated overall and by country, World Bank income group (WBIG), and geographic region. Multivariable Poisson regression models were used to estimate relative risk (RR).

Results

Overall, prevalence of diabetes in 29 LMICs was 7.5% (95% CI 7.1-8.0) and of undiagnosed diabetes 4.9% (4.6-5.3). Diabetes prevalence increased with increasing WBIG: countries with low-income economies (LICs) 6.7% (5.5-8.1), lower-middle-income economies (LMIs) 7.1% (6.6-7.6), and upper-middle-income economies (UMIs) 8.2% (7.5-9.0). Compared with no formal education, greater educational attainment was associated with an increased risk of diabetes across WBIGs, after adjusting for BMI (LICs RR 1.47 [95% CI 1.22-1.78], LMIs 1.14 [1.06-1.23], and UMIs 1.28 [1.02-1.61]).

Conclusions

Among 29 LMICs, diabetes prevalence was substantial and increased with increasing WBIG. In contrast to the association seen in high-income countries, diabetes risk was highest among those with greater educational attainment, independent of BMI. LMICs included in this analysis may be at an advanced stage in the nutrition transition but with no reversal in the socioeconomic gradient of diabetes risk.

SUBMITTER: Seiglie JA

PROVIDER: S-EPMC7085810 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Publications

Diabetes Prevalence and Its Relationship With Education, Wealth, and BMI in 29 Low- and Middle-Income Countries.

Seiglie Jacqueline A JA Marcus Maja-Emilia ME Ebert Cara C Prodromidis Nikolaos N Geldsetzer Pascal P Theilmann Michaela M Agoudavi Kokou K Andall-Brereton Glennis G Aryal Krishna K KK Bicaba Brice Wilfried BW Bovet Pascal P Brian Garry G Dorobantu Maria M Gathecha Gladwell G Gurung Mongal Singh MS Guwatudde David D Msaidié Mohamed M Houehanou Corine C Houinato Dismand D Jorgensen Jutta Mari Adelin JMA Kagaruki Gibson B GB Karki Khem B KB Labadarios Demetre D Martins Joao S JS Mayige Mary T MT Wong-McClure Roy R Mwangi Joseph Kibachio JK Mwalim Omar O Norov Bolormaa B Quesnel-Crooks Sarah S Silver Bahendeka K BK Sturua Lela L Tsabedze Lindiwe L Wesseh Chea Stanford CS Stokes Andrew A Atun Rifat R Davies Justine I JI Vollmer Sebastian S Bärnighausen Till W TW Jaacks Lindsay M LM Meigs James B JB Wexler Deborah J DJ Manne-Goehler Jennifer J

Diabetes care 20200212 4

<h4>Objective</h4>Diabetes is a rapidly growing health problem in low- and middle-income countries (LMICs), but empirical data on its prevalence and relationship to socioeconomic status are scarce. We estimated diabetes prevalence and the subset with undiagnosed diabetes in 29 LMICs and evaluated the relationship of education, household wealth, and BMI with diabetes risk.<h4>Research design and methods</h4>We pooled individual-level data from 29 nationally representative surveys conducted betwee ...[more]

PMID: 32051243

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Project description:IntroductionLow levels of household access to basic environmental health assets (EHAs)-including technologies such as clean cookstoves and bed nets or infrastructure such as piped water and electricity-in low- and middle-income countries (LMICs) are known to contribute significantly to the global burden of disease. This low access persists despite decades of promotion of many low-cost, life-saving technologies, and is particularly pronounced among poor households. This study aims to characterize variation in access to EHAs among LMIC households as a function of wealth, as defined by ownership of various assets.MethodsDemographic and Health Survey (DHS) data from 41 low- and middle-income countries were used to assess household-level access to the following EHAs: 1) improved water supply; 2) piped water supply; 3) improved sanitation; 4) modern cooking fuels; 5) electricity; and 6) bed nets. For comparison, we included access to mobile phones, which is considered a highly successful technology in terms of its penetration into poor households within LMICs. Ownership levels were compared across country-specific wealth quintiles constructed from household assets using bivariate analysis and multivariable linear regression models.ResultsAccess to EHAs was low among the households in the bottom three quintiles of wealth. Access to piped water, modern cooking fuels, electricity and improved sanitation, for example, were all below 50% for households in the bottom three wealth quintiles. Access to certain EHAs such as improved water supply and bed nets increased only slowly with concomitant increases in wealth, while gaps in access to other EHAs varied to a greater degree by wealth quintile. For example, disparities in access between the richest and poorest quintiles were greatest for electricity and improved sanitation. Rural households in all wealth quintiles had much lower levels of access to EHAs, except for bed nets, relative to urban households.ConclusionsThe findings of this study provide a basis for understanding how EHAs are distributed among poor households in LMICs, elucidate where inequalities in access are particularly pronounced, and point to a need for strategies that better reach the poor, if the global environmental burden of disease is to be reduced.

Project description:IntroductionThe prevalence of smoking is unequally distributed across certain groups. One significant dimension is education inequality, where higher smoking prevalence is generally observed in lower-educated groups. However, studies investigating educational inequality are mostly associative. Meanwhile, studies carrying out a causal investigation focus typically on developed countries. In this study, we consider a panel of low-and-middle-income countries (LMICs) to investigate the causal link between education and smoking behavior.Aims and methodsWe use detailed micro-level household surveys for 12 LMICs where the duration of compulsory schooling has been extended. By identifying the individuals subject to higher compulsory schooling and using the exogenous variation in education caused by the increase in the duration of compulsory schooling, we estimate the causal impact of education on tobacco consumption. We rely on regression analysis to estimate the effect.ResultsOur results reveal that those subject to higher years of compulsory schooling have lower smoking-related outcomes, suggesting that higher education significantly lowers tobacco consumption in LMICs. The effect is primarily observed for women, where, for instance, higher compulsory schooling reduces the probability of smoking by 23% and the number of cigarettes smoked by 27%.ConclusionsThe study's results establish the causal link between education and smoking behavior in LMICs. This significant impact suggests that education policy is still an important tool to help reduce tobacco consumption, especially in settings where the average level of education is not high initially. Moreover, discouraging men from smoking requires other measures to complement education policy.ImplicationsEducation might help reduce tobacco consumption. However, studies-primarily for developed countries-find mixed results. This paper investigates the causal role of education on smoking in LMICs. Education reduces tobacco consumption, especially for women. Thus, education policy can be effective in low-education settings. Nonetheless, education policy should be accompanied by other policies to discourage men from smoking.

Dataset Information

Diabetes Prevalence and Its Relationship With Education, Wealth, and BMI in 29 Low- and Middle-Income Countries.

Objective

Research design and methods

Results

Conclusions

Publications

Diabetes Prevalence and Its Relationship With Education, Wealth, and BMI in 29 Low- and Middle-Income Countries.

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